Short Video Recommendation Method Based on Sentiment Analysis and K-means++

With the explosive growth of short video content, effectively recommending videos that interest users has become a major challenge. In this study, a short video recommendation model based on barrage sentiment analysis and improved K-means++ was raised to address the interest matching problem in short video recommendation systems. The model uses sentiment vectors to represent bullet content, clusters short videos through sentiment similarity calculation, and studies the use of clustering density to eliminate abnormal sample points during the clustering process. The study validated the effectiveness of the raised model through simulation experiments. The outcomes denoted that when the historical data size increased to 7000, the model's prediction accuracy could reach 0.81, recall rate was 0.822, and F1 value was 0.832. Compared with the current four mainstream recommendation algorithms, this model showed advantages in clustering time and complexity, with clustering time reduced to 8.2 seconds, demonstrating the efficiency of the model in raising recommendation efficiency and accuracy. In summary, the model proposed in the study has high recommendation accuracy in short video recommendation systems and meets the real-time demands of short video recommendation, which can effectively raise the quality of short video recommendations.